Solution-Binding and Molecular Docking Approaches Combine to

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Solution-binding and molecular docking approaches combine to provide an expanded view of multidrug recognition in the MDR gene regulator BmrR. Drew Gunio, John Froehlig, Katerina Pappas, Uneeke Ferguson, and Herschel Wade J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00704 • Publication Date (Web): 28 Jan 2016 Downloaded from http://pubs.acs.org on January 29, 2016

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Journal of Chemical Information and Modeling

Solution-binding and molecular docking approaches combine to provide an expanded view of multidrug recognition in the MDR gene regulator BmrR.

Drew Gunio, John Froehlig, Katerina Pappas, Uneeke Ferguson and Herschel Wade*

Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205

Running Title: Elucidating multidrug recognition in BmrR

*To whom correspondence should be addressed: Herschel Wade, Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland, USA, Tel.: (410) 502-5629; Fax: (410) 502-6910; Email: [email protected]

1 ACS Paragon Plus Environment


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ABSTRACT:

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Solution binding and molecular docking have been combined with a

diverse collection of chemical probes to further elucidate multidrug recognition in BmrR. Whereas previous efforts have focused on structural elucidations of MD binding, the present study examines features imparted by structure, including the recognition properties of the ligand-pocket, ligand structural requirements and key factors that define and influence binding.

Whereas MD-pockets are generally believed to be

featureless and very hydrophobic, logKD—clogP correlations observed for BmrR and other polyspecific proteins suggest polar contributions are required for broad-spectrum recognition of amphipathic ligands. We show that molecular docking simulations


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recapitulate key features of MD recognition and have been employed to further inform contributions from structure. In addition to elaborating our understanding of the structures and functional roles of pocket elements that dictate broad-spectrum binding, molecular docking has implication additional features that likely play major roles, including ligand dynamics and multiple ligand-binding modes.

KEYWORDS: multidrug recognition, antibiotic resistance, chemical resistance; molecular modeling, xenobiotic, molecular docking, ligand-binding protein, substrate specificity; functional redundancy

Abbreviations MDR, multidrug resistance; MD, multidrug; vdw, van der Waals, LEDP, lowest energy docking pose; RMSD, root mean squared deviation; RSCC, real space correlation coefficient; LBMs, ligand-binding modes; DME, drug-metabolizing enzymes; HB, hydrogen-bonding; ABC, AWalker binding cassette; MATE, metabolite and toxin extrusion; MFS, major facilitator superfamily; PXR, pregnane receptor; CAR, constitutively activated receptor



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Introduction

Multidrug (or xenobiotic) efflux actively removes foreign and cytotoxic compounds from intracellular environments.1-3 For normal functioning cells, multidrug (MD) efflux is universal, chemoprotective and essential.4-6 On the other hand, elevated levels of drug efflux protect drug-targeted cells and render them resistant to multiple therapeutic agents. MD transport has been established as a primary cause of MDR in an expanding list of pathogenic bacteria, parasites and fungi, including Pseudomonas aeruginosa, Salmonella enterica, Candida

albicans and Plasmodium falciparum.7-9 For higher eukaryotes, broad resistance to cancer chemotherapy drugs has also been connected to high levels of drug efflux. Three ABC transporters are believed to play significant roles, including P-glycoprotein (ABCB1).10

Contributions to MDR mechanisms control the outward flux of chemical agents by direct and indirect mechanisms.11 Levels of chemical efflux are influenced directly by energydependent pumps, which are required for uphill transport across cell membranes.

Gene

regulatory and other signaling proteins modulate efflux via control of pump expression.12, 13 At the cellular level, ligand-responsive efflux and sensor proteins provide broadly


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chemoprotective responses that detect and extrude toxic compounds from the inside of selectively target xenobiotics and other drug-like chemicals.14 Native biological ligands, including enzyme cofactors and metabolites appear to be left somewhat untouched. Despite their roles in dictating drug disposition, toxicity and efficacy, MDR regulator and efflux functions remain incompletely understood with regards to ligand recognition and ligand-controlled allostery. Incidentally, our lacking knowledge of the broadly selective functions continue to hinder the successful development of small-molecules that effectively modulate efflux-mediated MDR.

Current descriptions of MD recognition by MDR proteins draw from investigations of ligand interactions with efflux pumps15-19 and regulator proteins20-24 and xenobiotic metabolizing enzymes (DMEs).25-29 In each case, structural, solution-based and functional methods were combined with diverse sets of chemical probes. This strategy has enabled the identification of putative MD binding pockets and key details of MD recognition, including shared pocket features that distinguish MDR and other polyspecific proteins from highly selective counterparts. Some of the identified features define the ‘canonical’ view of MD recognition, which equates broad-spectrum binding with large cavities volumes (ca. >1200 Å3), flexible



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pocket elements as well as overlapping, distinct mini-pockets.30, 31 The high recurrence of these features strongly supports the assigned roles as major MD recognition determinants. However, demonstrations of MD recognition in MDR systems displaying smaller pockets (volumes ca. < 1000 Å3) and limited flexibility20, 32 expose the restricted applicability of “canonical” and other models that focus entirely on protein architectures. Structural commonalities, not related to protein architecture, have been identified in both “canonical” and non-canonical systems; such features also appear to inform MD recognition.11 For example, overrepresented sets of aromatic, and to a lesser extent, aliphatic residues are prevalent in MDR proteins.33 X-ray analyses of broadly specific recognition illustrate the versatile bonding properties of both aromatic and aliphatic moieties, which engage with a variety of nonpolar ligand groups in variable geometries. Aromatic residues are the more adaptable of the two and also partake in weakly polar interactions with charged ligand moieties.34 Polar and charged residues contribute less to polyspecific binding due to strict bonding geometric requirements that promote specificity. When polar contacts are observed, well-known restrictions are reduced by the participation of solvent molecules and conformationally flexible residues. The importance of versatility in multi-specific binding extends to electrostatic interactions. In such cases, charged protein residues are placed


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strategically in distal regions of binding pocket and neutralize cationic centers largely via long-range interactions. Structural, solution-based and sequence analyses of the polyspecific efflux support suggest drug-pocket features and binding strategies for the membrane embedded pumps. Despite advances in our knowledge of MD recognition, descriptions of MD recognition remain incomplete with respect to features identified particularly their roles drug binding. For most MDR systems, identifying pocket elements that match those described above is relatively straightforward; however, understanding their contributions to MD recognition and formulating universal binding models that employ all identified pocket elements remains a significant challenge.

BmrR is a MDR regulator from Bacillus subtilis that is widely regarded as a bacterial prototype for investigating MD recognition (Figure 1).35, 36 It also controls the expression of the Bmr efflux pump in response to unrelated lipophilic cations and other compounds.37, 38 In contrast to canonical views of MDR functions, BmrR supports MD binding in a small, rigid pocket. To date, investigations of ligand interactions in the non-canonical BmrR pocket have utilized a battery of approaches, including chemical probes. Together, these studies have demonstrated both broad specificity as well as a strong preference for lipophilic



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cations. More recently, X-ray crystallography, combined with solution-binding experiments, have identified key defining features of MD recognition in the MDR regulator, including several well-defined, pocket elements that appear to dictate recognition of multiple, unrelated ligands.

Whereas such insights represent significant advances, the binding

properties of the BmrR pocket are not fully understood in terms of the ligand-pocket structure or the contributions of identified recognition elements.

In this study, we have further elucidated MD recognition by BmrR using an approach that combined solution-binding experiments, molecular docking simulations and diverse chemical probes. By using solution-binding approaches and analyses, we focus more on features imparted by structure, including the recognition properties of BmrR, including ligand structural requirements and molecular contributions to binding. Molecular docking simulations and subsequent analyses have yielded elaborated descriptions of structural contributions. Finally, the results of this study offer a detailed, molecular picture of MD recognition that relates ligand-pocket structure to the observed binding properties.

Results


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Chemical probes of MD recognition. We employed a collection of structurally diverse probes in an investigation of MD recognition in BmrR. The ligands chosen compose the MDR and nonMDR sets. Together, both cover a substantial amount of the chemical space defined by known ligand interactions with MDR proteins. The MDR set (1-15) includes antibiotics, anti-tumor agents, efflux tracer dyes and MDR inhibitors. Ligands of this set have been used widely to investigate MDR functions (Figure 1).18, 39-41 Members of the MDR ligand set are largely cationic (1, 2, 4, 5, 7, 8, 10-12, 14) or have a net zero charge (3, 6, 9, 13 and 15). Whereas the charge differences are minimal, other properties vary considerably across the set. These include hydrophobicity, aromatic content, molecular shape, hydrogen bonding capabilities and others.

The nonMDR ligands (16-42) have not been widely used to study MDR proteins. They have been employed because they display characteristics not well represented by the MDR set. Notable features include aliphatic characteristics (16-17, 19-21), neutral scaffolds (28-34) as well as polycationic (22-27) and anionic frameworks (35-40). The collection also includes a mini-series of structurally related rhodamine analogs (41-44) that exhibit different charged properties. The nonMDR set is more diverse than the MDR series. However, both



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combined to define an extended collection displaying a larger repertoire than those previously employed.

Fluorescence-based detection of ligand interactions in BmrR. Tyrosine residues dominate the intrinsic BmrR fluorescence (λEX = 280 nm), which includes an intense, homogeneous emission band (λMAX = 305 nm). In the presence of known activators and other ligands, the BmrR fluorescence is partially quenched. Details of ligand-induced quenching by two probes are summarized in Figures 2A and 2B. For 2, 11 and other interacting probes, quenching is widely applicable, concentration-dependent and saturable. On the other hand, each interacting probe exerts distinct effects on BmrR. For example, binding of 2 produces a 53% fluorescence decrease. In contrast, a 10% decrease is observed for 11. The observed quenching efficiencies do not correlate with the ligand affinities. However, they do appear to be related to ligand structure. All KD determinations take into account ligand saturation and quenching. The fluorescence-based KD values determined for 1 and 4 reproduce previously reported values determined by other methods.20, 32, 37


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To determine if the quenching-based KD values reflect binding specifically at the putative effector-pocket, we also employed a competitive, anisotropy-based method in which RH6G1, the known inducer, is displaced from BmrR by different ligands.42 As expected, the anisotropy of the 1 emission decreases with increasing concentrations of competing ligands. In nearly all cases, the overall change in anisotropy was invariant. Binding isotherms are shown for 15 and 18 (Figures 2C and 2D). For both and other interacting ligands, the data are well described by the model involving competitive binding at a single site (see Materials and Methods). In most cases, the anisotropy-based data also reproduced the quenching results. For a few probes, including, 10, 14, 19 and 20, the quenching data suggest significantly tighter binding than determined by the competitive method. The observed differences suggest that the quenching approach detects binding events occurring outside of the C-terminal ligand-pocket.

Solution-binding analyses of chemical recognition by BmrR: MDR probes.

The

fluorescence quenching and anisotropy-based methods detected binding by all examined MDR probes. The observed KD values were non-identical and show a modest range (0.52 μM to 84 μM) of 160-fold (Table 1). Cationic probes dominate the high affinity regime for



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the MDR series, which is consistent with preferential recognition of positively charged species. These include three aromatics efflux dyes, 1 (0.52 μM), 5 (1.9 μM), 2 (2.3 μM) and the large macrolide, 12 (3.5 μM). Interestingly, the weakest binders of the MDR set, 4 (81 μM), 7 (84 μM) and 10 (54 μM), are also cationic. The remaining probes define the midaffinity regime for the MDR set. These include, 6 (29 μM), 8 (17 μM), 9 (16 μM), 11 (28 μM), 13 (6.7 μM), 14 (17 μM) and 15 (6.5 μM). Probes defining this regime display the broadest range of chemical structures including, aromatic, aliphatic and cationic, uncharged and zwitterionic features. The range of KD values (5-fold) demonstrate a weak dependence of ligand structure on binding.

nonMDR probes. The more diverse nonMDR series exhibits a notably larger KD range (8200-fold) than the MDR set (Table 2). As observed for the latter, the nonMDR tight binding regime is defined by in part by highly aromatic monocations 16 (1.8 μM), 17 (2.1 μM), 19 (3.1 μM), and 41 (1.9 μM) as well as aminoglycosides, 22-27, which exhibit KD values ranging from 0.75 to 1.1 μM. Of the two subsets, the polycations (median value of 0.9 μM), as a group, display higher affinities than the tightest binding monocations (median

KD value of 1.9 μM). Three aliphatic cations, 19-21, were employed to address ligand


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flexibility and charge influences on binding. TEA-19 (5.4 mM), the smallest probe of the collection also binds the weakest of all cations. The addition of alkyl chains to 19 yielding TEB-20 and TBA-21 results in a modest (10- and 0.2-fold) binding enhancements and KD values of 490 and 407 μM, respectively.

The charge-neutral (uncharged) ligands (29-33, 42-44) dominate the mid-affinity regime for the nonMDR set. They exhibit a median KD value of 21 μM, which mirrors that observed for MDR counterparts (17 μM). Despite considerable differences in the sizes and shapes of the ligands defining this group, their affinities are almost invariant. Two probes exhibit KD values outside of this regime. The first, SUC-33 (KD value of 125 μM), is highly polar, uncharged and non-aromatic. The second, AMP-34, is an amphipathic, zwitterion that exhibits a KD value of 120 μM.

Anionic probes. The anionic probes (34-38) are the weakest binders (median value > 3.8 mM) of those examined. In all cases, weak binding and strong ligand absorptions hampered KD determinations by fluorescence quenching. However, the anisotropy-based method afforded KD values for the two aromatic anions, HAB-34 (890 μM) and ANS-35 (420



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μM); the KD value estimated for the polyanion, AMP-36 (3.8 mM) appears to be at or near the limit of detection by the latter method. The displacement of 1 was detectable at only very high ATP-37, NAD-38 concentrations (>3 mM). The use of the lower affinity probe, 43, produced similar estimations. For both, lower limits for their KD values were estimated to be >6mM based on the measured 1 displacement. Finally, CAR-40, an anionic β-lactam antibiotic exhibited no appreciable binding.

Molecular docking-based analyses of MD recognition. A docking approach was used to further address MD recognition and uncover structural details of broad specificity in BmrR. MDR (3, 7, 10, 12, 14 and 15) and nonMDR (16, 18-24, 26, 29-32, 36, 38 and 39) probes, for which no structural data for binding currently exists, were used in these studies. Docking results were also obtained for additional six ligands (1, 4, 5, 8, 9 and 11) for which the details of binding are known. To assess the correspondence between the docking and experimental data, the binding modes predicted for six ligands were compared to those revealed by X-ray diffraction. Five (1, 4, 5, 8 and 9) of the six the lowest energy docking poses (LEDPs) showed good agreement with the binding modes revealed by experiment (Figure 3A). For 11, the third-ranked pose (ΔΔGDOCK ca. 0.2 kcal/mol relative to the LEDP)


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closely reproduces the X-ray results. The relative ligand heavy atom positions for the docked and X-ray structures are shown in Figure 4A. Relative to the binding modes observed, docked 1, 4, 5, 8, 9 and 11 exhibit RMS displacements of 2.5, 3.7, 3.1, 2.1, 1.9 and 3.2 Å, respectively. The predicted 1, 8 and 9 binding modes faithfully replicate the interactions observed in the crystals. For 4, 5 and 11, larger departures from experimentally determined binding modes. In such cases, the predicted binding modes show observed contacts exchanged for comparable interactions with a set of nearby residues (Figure 3B).

The predicted energetics of binding concur with those determined in solution studies. This comparison focuses on ligand rankings (based on the computed binding energies) due to the inability of docking methods to reliably predict binding energetics.42-44 For BmrR, the ndocking- and experimentally-based rankings show a direct correlation (slope = 1.1 ± 0.3, R = 0.991) (Figure 4A). The analysis reveals two outliers, VER-10 and RES-14, both of which show significantly larger than predicted (2.6 and 2.9 kcal/mol, respectively) binding energies. For the remaining probes, the predicted and observed values differ by ca. 1.1 kcal/mol. A similar relationship (slope of 0.79 ± 0.16; R = 0.75)) is observed for the docking and experimental binding energies (except 10 and 14) (Figure 4B).



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Strong agreements between the docking predictions and experimentally defined aspects of ligand binding implicate former as a useful approach for probing and elucidating MD recognition by BmrR and MDR proteins. When combined with diverse probes, the high utility of the approach for interrogating structural and functional roles of potential recognition determinants becomes apparent. The details of docking for a representative set of BmrR probes are summarized in Figure 5. The corresponding crystallographic details are presented in Figure S1.

Moreover, Figures 6A and 6B present docking and X-ray diffraction

based depictions of recognition, each of which superimpose the predicted and observed probe LBMs revealed by each method. Close analyses of the pocket elements implicated by both sets reveal a faithful recapitulation of the description of MD recognition by the docking approach. Both identify nearly identical recognition elements, including the aromatic platform and hydrophobic (Hb) pincer. The similarities extend somewhat to HB contacts and electrostatic interactions. Among the interactions examined, docking reveals at least HB two sites in addition to those observed by crystallography. The new contacts include the main chain of E145 and hydroxyl group of Y229 (Figure 4C). Such differences


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are consistent with a higher investigative capacity of the docking probe set, which is larger and more structurally diverse than the set of probes presented in the crystal structures.

A complete picture of HB in MD recognition by BmrR has not been elucidated. To date, a total of six sites have been reported.20 They interact with multiple ligands and derive from the side chains of E253, N149, Y152 and Y187, main chain of L148 and one pocket-bound water molecule. However, given the binding affinities and diverse HB properties of the extensive set of examined probes, it appears unlikely that identified set of sites alone completely defines the HB capacity of BmrR. As such, we exploited the diversity and HB capabilities of selected docking probes to uncover undisclosed HB contact sites in BmrR. Here, we focus on three probes including, DOX-7, ERY-10 and SRY-24. Each presents a unique HB atom configuration as well as large HB atom counts of ten, fourteen and nineteen, respectively. All three probes also display different donor—acceptor ratios. The predicted numbers of HB contacts for 7 (7), 10 (4), and 24, (7) are considerately larger than those observed by X-ray analyses. The contacts also include five of the six previously identified sites and six new ones. As a group, the complete set of docking probes identifies a total of eighteen ligand-accessible HB sites, which includes an overrepresented set of



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acceptor sites and a dearth of sites that act as HB donors (Figure 4D). The predicted HB sites are dispersed throughout the ligand-pocket. However, they appear to be more equally distributed along the long axis of the pocket. The docking, solution binding and X-ray data are consistent with a highly versatile solvation function that adapts to a variety of ligands HB properties. Interestingly, nearly every ligand-docked complex reveals at least one buried, unpaired ligand HB group. This is not surprising given the rigid nature of the proteinderived solvation shell. This suggests a potential, key role for water in MD recognition. To date, solvent of this type has not been readily identified owing to the modest diffracting properties of the ligand-bound BmrR crystals.

Structural studies have implicated three acidic residues, D47, E253 and E266, in the broad recognition of diverse cations.20, 32 Whereas the docking results support key roles for all three (Figure 4C), they also suggest a fourth player, E145. A number of observations support a bonafide cation neutralization role for E145. Like D47, E253 and E266, E145 appears poised to neutralize the cationic centers for a number of probes, including NOR-3, DOX-7, RES-14, TEA-19, TEB-20, SCM-22, HYB-23, SRY-24, PAR-26, and NEO-27. Moreover, further examination of the tetrad carboxylates and their interactions with the examined


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probes does not significantly distinguish E145 from D45, E253 or E266. However, E145 is analogous to E266 with regards to cation neutralization. Figure 7 shows the predicted interactions between the carboxylate tetrad and bound ligand cation centers. All of the cationic centers show close-to-moderate approaches (ca. 4 to 8 Å) to at least one of the four side chains of the carboxylate tetrad (Figure 4C) whose pseudo symmetric arrangement underlies the structural redundancy and broad cation selectivity of the electrostatic determinant. The tetrad also appears to be involved in the broad recognition of zwitterionic compounds. In such cases, K2, located immediately outside the MD binding pocket, is predicted to be involved.

Docking analyses of ligand-binding modes and degenerate chemical recognition. date, crystallographic models of BmrR show a single binding mode per ligand.

To

Docking

simulations, on the other hand, identify multiple, poses (or binding modes) exhibiting comparable binding free energies (within 1.0 kcal/mol of the LEDPs). Among each isoenergetic set of poses are distinct binding modes showing wide range departures from the LEDPs. For flexible probes (i.e., PUY-8, VER-11 and DAN-16), the number of binding



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modes predicted was as large as twelve. Fewer were suggested for rigid and smaller ligands (i.e., TPP-4, ET-5 and JOU-28).

Examples of multiple ligand-binding modes (LBMs) have been described for a number of MDR proteins, however, they remain largely unexplored.22, 45-47 However, our docking results offer another opportunity to investigate this type of binding. Comparative analyses of BmrR interactions with multiple ligands and those with a single ligand (i.e. ERY-12, Figure 6C) predicted to adopt multiple, distinct LBMs highlight strong analogies between the two types of binding and a relevant role for multiple LBMs in MDR functions. Key features shared by both include contacts with the Hb pincer, aromatic platform and HB elements.

Both also

appear to utilize the complete carboxylate tetrad. Small departures from MD recognition are observed. Most notably, each group engages with unique sets of HB contacts. This is likely the results of differences in the examined ligand sets and not the modes of binding. Overall, the recognition of multiple, isoenergetic LBMs likely employs the same set of elements and recapitulates the features that define MD recognition.


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Crystallographic data support the docking results. Whereas the previously reported LBMs likely reflect those most highly populated in the crystals, it is also possible to carry out X-ray refinements with alternative docking poses for 5, 8, 11 and 18 using the weak difference electron density corresponding to each ligand (Figure 7). In all cases, the input LBMs remained unchanged by the refinements. For 5 and 18, departures between the examined LBMs relate strictly to orientation. For 8 and 11, different ligand-bound orientations and conformations are observed. Despite such divergences, comparable real-space correlation coefficients (RSCC) are observed across each pose set suggesting that the modeled LBMs are probably populated to some extent; this is also supported by the refined B-factors, which remain invariant across each LBM set.

Dissecting the interplay between ligand structure and binding. Whereas BmrR displays broad-spectrum binding, the non-identical probe KD values underscore the significance of the differential recognition of variable ligand features. To better define the influences of ligand structure in BmrR, we examined ligand binding against a battery of ligand descriptors. Only a few number parameters showed modest correlations to ligand recognition. However, this is not surprising given the high chemical and structural diversity



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of the examined ligand set.48 Figures 8A-8D, 8H present the results in the form of box plots, which summarize the data as distributions of binding energies for the probes displaying different counts of atom types. Each distribution is divided into quartiles with the two boxes depicting the second and third. Both boxed quartiles are separated at the median values. For ligand displaying more than one HB acceptor, we observe a decrease in binding with increasing number of that atom type. We observe a similar trend for oxygen atom count. On the other hand, the opposite trend is found for nitrogen and aromatic atom count, the latter of which shows a smaller increase in binding across the series. In Figures 8E-8G, binding is analyzed with respect to ligand hydrophobicity. Whereas the statistics are rather modest (R = 0.51) for the correlation between ligand hydrophobic van der Waals surface area (VSA) and binding, a comparison of both clearly show a linear correlation (slope = - 0.005 ± 0.002 kcal/Å2) (Figure 8E). Both the correlation and small slope value are supported by the logKD—clogP relation produced by the solution-binding data for the cationic and neutral ligands. This correlation spans a wide 12 clogP units, where clogP represents the logarithm of calculated octanol—water partition coefficients for the examined MDR and nonMDR probes. An analysis of MDR and nonMDR ligands showed good statistics (R = 0.79) and a slope value of -0.23 ± 0.05 (Figure 8F). A similar analysis for


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QacR, an unrelated MDR regulator from S. aureus also reveals a linear logKD—clogP correlation (slope = -0.17 ± 0.05, R = 0.69) (Figure 8G).39

The observed logKD—clogP correlation did not extend to all examined probes. For instance, in the case of BmrR (and QacR), 4, a widely used MDR probe exhibits significantly (ca. 100fold) weaker binding than predicted by its calculated hydrophobicity (clogP) value (Figures 8F and 8G). Additional cationic ligands show disconnects between binding and ligand hydrophobicity. These include 10 and 14 as well as the highly flexible, aliphatic probes, 1921. Despite being the least hydrophobic probes examined, the aminoglyosides, 22-27, were among the tightest binders.

The influence of electrostatics on ligand recognition was addressed using two approaches. The first involved a global analysis of ligand charge preference. As shown in Figures 8A-8D, box plots were used in this analysis (Figure 8H). Of the probes examined, we observe a strong preference for those displaying a net positive charge. Polycations, on average, appear to bind more tightly (median 8.2 ± 0.1 kcal/mol) than monocationic analogs (median 7.3 ± 0.1 kcal/mol), which show a wider range of binding energy values. Cations, as a



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group, bind tighter than the charge-neutral subset (median 6.3 ± 0.2 kcal/mol) that includes both zwitterions and uncharged probes. Anionic probes are the weakest binders of the examined ligand collection (median, 3.8 ± 0.2 kcal/mol). Importantly, the distributions exhibited by the charge-neutral, cation and polycation subsets show substantial overlap. However, binding energies exhibited by the anions are not coincidental with any subset and are consistently weak. The observed electrostatic effects are consistent with the observed correlations between binding and ligand nitrogen and oxygen atom contents.

A more direct method for deducing the role of ligand charge involved of investigation of salt-linked effects on 1, 22 and 26 interactions with BmrR. At pH 7.5, these probes display formal charges of +1, +2 and +5, respectively. Figure 8I presents the binding affinities for all three probes under varying KCl concentrations. Each probe exhibits salt-dependent binding. The dependence is largest for 26, which displays the highest charge (slope = 0.28 ± 0.02). The slope values revealed by the lnKD—ln[KCl] correlations for 1 (slope = 0.16 ± 0.03) and 22 (slope = 0.21 ± 0.06) indicate reduced salt dependencies relative to 26. These results are consistent with significant binding contributions from long-ranged electrostatic interactions.


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Discussion The features that dictate MD recognition in BmrR have been addressed using an approach combining solution-binding, molecular docking and structural analyses. Analyses of MD binding using solution-binding experiments and diverse chemical probes have elucidated, in considerable detail, features of MD recognition imparted in large part by previously identified and subsequently elaborated structural elements that dictate interactions with multiple, unrelated ligands. These include the recognition properties of BmrR bindingpocket, ligand-structural effects and molecular influences on binding. Molecular docking simulations have informed detailed elucidations of the pocket elements contributing to the observed BmrR functions. Insights illuminated here leave MD binding understood in terms of key molecular and chemical contributions that inform relationships that link ligand-pocket structure and recognition.

Until now, broad-spectrum recognition by BmrR was almost entirely implied, drawing from a small database of ligand interactions. However, solution analyses of BmrR interactions with our probe collection firmly demonstrate broad-spectrum recognition in the small, rigid BmrR



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pocket. Surprisingly, appreciable binding is detected nearly all of the probes examined, which include a wide-range of clinically relevant agents and ligands less likely to be encountered by MDR proteins. Every examined cationic, charge neutral and zwitterionic probe showed measurable binding; only five exhibit KD values greater than 100 μM. In contrast, only two of the six anions display KD values of < 1 mM. The remaining anions bind with KD values at or below the limit of detection (> 3 mM). Excluding the anions and other weakly binding ligands, the collection of probes display a low median KD value of 6.6 μM and modest affinity range (KD,MAX/KD,MIN ~ 250) relative to the repertoire of structures examined. Overall, the affinities of clinically prescribed agents fully agree with a relevant MDR role and previously reported data for other MDR proteins, including those displaying canonical features. Importantly, quantitative descriptions of ligand binding by BmrR have enabled instructive analogies to other well-characterized bacterial regulator (QacR39, AcrR41) and efflux systems (QacA49, EmrE40 and MdfA18). For example, ligand interactions with two unrelated MDR regulators, QacR and AcrR, as well as the SMR pump, EmrE, nearly reproduce the recognition profile, mean KD value (8.6, 8.3 and 11 μM, respectively) and affinity range (KD,MAX/KD,MIN ~ 250-300) observed for BmrR. These analogies extend to QacA, MdfA and other efflux systems that have been investigated using MDR-like probe sets.


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Broad recognition by BmrR is underscored by appreciable interactions with a wide range of molecular shapes and structures presented by ligands dominated by aromatic and aliphatic characteristics as well as ligands that combine both. Polyspecificity in BmrR is also broad in scope with respect to ligand hydrophobicity, charge and HB capabilities. For BmrR, the affinities determined for flexible, aliphatic and anionic probes likely represent the low and high affinity limits for cations and anions, respectively. Although the diverse probe did address key ligand structural requirements, the level of detail was not particularly high. However, a slight preference for highly aromatic ligands was observed, which may reflect intrinsically stronger BmrR contacts with aromatic ligand groups or the reduced entropic penalties associated with binding rigid ligands. Since this preference is not very large, the latter effect likely offers an small advantage over tighter packing. Additional influences on binding relate ligand size, ligand burial of vdW surface area and HB properties. Increases in the first three parameters result in higher binding affinities. In contrast, binding appears to weaken with an increasing number of ligand HB acceptor sites. This agrees with the BmrR structure, which shows a large number of accessible, HB acceptor sites lining the ligandpocket.

A similar result is observed for ligand HB donor sites, suggesting that the observed



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binding changes may not be related to HB acceptor or donor site number, but to reduced ligand hydrophobicity.50 Interestingly, the trends observed for nitrogen and oxygen atom counts appear to be consistent with the relationship between ligand binding and charge.

MDR ligand-binding pockets require hydrophilic components. The linear logKD—clogP relationship observed for BmrR ligands somewhat agrees with the general consensus that MD and xenobiotic binding-sites are largely hydrophobic (vide infra). For BmrR, this relationship is defined by a majority of the examined probes with the exception of the aminoglycosides (22-27) and ligands predicted to adopt alternative binding modes. The bulky 4 is known to bind in a mode that prevents efficient ligand burial. The predicted LBMs confine 19-21 to a small region of the binding pocket. In contrast, 10 and 14, likely access binding-modes involving a newly identified mini-pocket. On the basis of the docking results and observed formal charges and HB properties, the aminoglycosides (22-27), are predicted to interact with BmrR differently from ligands with formal charges smaller than two. Finally, linear logKD—clogP correlations are typically obtained for probe sets displaying a shared structural core and with small perturbations. The present study, however, presents


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a linear correlation (R = 0.91) defined by an unusually diverse set of probe that apparently conforms to a common binding scheme.48

Whereas influences of hydrophobic-related effects in BmrR are demonstrated by the linear logKD—clogP correlation defined by a series of ligands, the observed slope value does not suggest a dominant role. Indeed, similar studies of biological systems displaying optimized nonpolar contacts typically report large clogP—dependencies on binding. For example, a linear logKkcat/KM—logP correlation with a slope of -1.2051 was revealed through studies of hydrolysis of a homologous series of peptide substrates by subtilisin BPN’. Investigations of the hydrophobic S1 pocket of another serine protease, α-chymotrypsin, yielded an even larger value of approximately -2.0.52 In contrast, inquiries of broadly selective proteins to date, including P-glycoprotein53, CYP3A4, CYP2D654 and the hERG potassium channel55, like BmrR, show small clogP—dependencies. Similar to QacR and BmrR, the values reported for these systems (-0.34, -0.38, -0.28 and -0.23, respectively) suggest that these pockets are approximately three to five times less hydrophobic than octanol and ten to twenty times less than that of enzyme pockets. Structure-activity (QSAR), crystallographic and other structural investigations support the lower than expected clogP—dependencies. Invariantly,



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each includes significant polar and electrostatic contributions. By visual inspection, the BmrR pocket appears to be largely hydrophobic. Apparently, contributions from a sizable set of ligand-accessible, HB donor and acceptor atoms reduce the hydrophobic properties of the pocket. An examination of contact potentials within the BmrR pocket reveals a microenvironment with considerable potential to interact favorably with polar atom types (Figure 9).

Analyses of the ligand-binding energies and non-polar VSA burial in BmrR support the observed logKD—clogP relation (Figure 8H). Based on the solution data, the change in ligand-binding free energy, as a function of total ligand VSA, is ca. 12 ± 3 cal/(mol•Å2). Previously, we determined a similar value of 7 ± 2 cal/(mol•Å2) based on structures of BmrR bound to different ligands20. Both values are small relative to the reported minimum contributions of 28 cal/(mol•Å2).56-59 We previously rationalized the discrepancy in terms of suboptimal ligand packing. Here, we refine our previous view to include polar contributions from the BmrR pocket. Finally, the clogP—dependencies on ligand binding for BmrR, QacR and other broadly selective systems may suggest a common, potentially optimized strategy for multi-specific ligand recognition. Moreover, if polar characteristics were completely


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absent from MD (xenobiotic) binding-pockets, broad-spectrum recognition would likely be restricted due to their inability to engage appropriately with ligand HB and charged moieties.

Recognition of multiple-ligand binding modes employs the same elements used for MD recognition. Addressing MD recognition is typically not a problem when using binding or other functional studies due to the ease of adapting such approaches to diverse arrays of ligands.15, 60 However, obtaining high quality crystals of multiple ligand-bound complexes of MDR systems is less straightforward and a common bottleneck for interrogations of drug binding by MDR proteins.11 Using docking to overcome this hurdle resulted in several favorable outcomes. First, the reproduction of key experimental results lead to the use of docking as useful proxy for further elucidating the details of multi-ligand interactions in BmrR. Moreover, whereas crystal structures offer defined, static pictures of binding, the dynamic views provided by docking may offer more realistic depictions of MD recognition. Re-analyses of data from previously solved structures, which included modeling of alternative binding modes of selected ligands into the electron density proximal in the BmrR pocket supports the docking results despite its small size and rigidity (Figure S1). Based on



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our results, the recognition of multiple LBMs would appear to be an inherent feature of MD recognition. Multiple LBMs have been reported for a number of MDR proteins, including PXR, CYP3A4, CYP2D6, QacR and AcrB.19, 27, 45-47, 61

Conclusion Both the features of MD binding and our lacking knowledge of physiological substrates and ligands of MDR proteins hinder efforts to fully elucidate the molecular details underlying the recognition of multiple, unrelated ligands, including those related to the binding properties of MDR protein pockets and elements that dictate the binding of diverse ligands. Whereas we continue to depend heavily on structural studies of ligand-bound MDR proteins, it has become clear that they alone cannot provide molecular pictures of MD recognition for a number of reasons. However, we have shown that combining structural information with solution-binding data and molecular docking analyses with an extended collection of chemical probes offers a powerful approach to elucidating MD recognition to a significant extent for BmrR. We propose that this strategy will likely do the same for other MDR systems62.


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Materials and Methods Chemicals. All reagents, including ligands were purchased from Sigma-Aldrich Company, Fischer Scientific, Bio-Rad Laboratories, Anaspec, Invitrogen, USB Corporation and Acros Organics. Spectrophotometic grade glycerol was purchased from RPI Corp. All Ligands were used without further purification.

Ligand clogP calculations. All clogP values (ligand water—octanol partition coefficients) were calculated using the XLOGP3 algorithm (AlogPS 2.1, VCCLAB).

Protein Production and Purification. His6-BmrR was expressed in E. coli (Top10) cells. Four hours after induction with 0.02% arabinose, the cells were pelleted and resuspended in HisTrap loading buffer. Treatment of the cells with lysozyme, EDTA, PMSF, protease inhibitors and DNAse was followed by microfluidization. The cell extracts were clarified by centrifugation (16,000 rpm), filtered and purified using an AKTA FPLC (GE Life Sciences). The purification scheme included nickel affinity (HisTrap HP), heparin affinity (HiTrap Heparin HP) and anion exchange (HiTrap Q HP) chromatography.



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Solution-binding Assays. The data for all ligand titrations were collected using Horiba Jvon Yvon Fluorolog-3 and ISS ChronosFD PC1 fluorimeters. Each experiment was carried at 24.5°C. All samples as well as ligand and protein stocks were prepared in degassed, filtered fluorescence buffer (200mM KCl, 25mM potassium phosphate, 1mM EDTA, 2mM DTT, 5% glycerol, pH 7.5). Ligands were dissolved in the fluorescence buffer. Poorly soluble probes required the addition of organic cosolvents (i.e. DMSO), which showed no effect on ligand binding. The BmrR and ligand concentrations of working stocks were determined spectrophotometrically. The molar absorptivity for BmrR (ε280nm = 32,780 L mol-1 cm-1) was calculated using Protparam (web.expasy.org). For the ligands, literature reported values were used.

Ligand Titrations. To monitor the intrinsic fluorescence in BmrR, we used an excitation wavelength of 280 nm. Emission data was collected from 295 to 450 nm (λEM,MAX

≈ 305 nm). Each experiment used both working and reference samples, the

latter was devoid of BmrR. UV-Vis spectra were also collected in order to quantify inner filter effects. Ten to twenty spectra were collected for each binding isotherm. Background, dilution and inner filter effect corrections were applied to the emission


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spectra to yield the corrected BmrR fluorescence. For each ligand, the corrected intensities at 305 nm were plotted against the total ligand concentration. The quenching data were fit to a binding model involving identical, independent sites (equation 1):

FCORR = FMAX − ∆F × ([L]TOTAL / ([L]TOTAL + K D )

(1),

where Fcorr is the corrected BmrR fluorescence at various known [L], Fmax is the initial (corrected) fluorescence intensity,

∆F reflects the quenching properties of each

ligand, [L]TOTAL is the ligand concentration and KD is the apparent ligand dissociation constant. KaleidaGraph 4.1 (Synergy) was used for the analyses, manipulation and curve fitting of the fluorescence quenching data.

Anisotropy of the RH6G (1) emission (λEX = 527 nm, λEM = 550 nm) was used to monitor its displacement from the BmrR ligand-pocket by diverse probes. A band pass filter (550 ± 20 nm, Newport Corp) was used in order to minimize scattering effects. For each titration, a solution of pre-formed BmrR•1 complex (RH6G-1, 160 nM; BmrR, 300 nM) was prepared (2.5 mL) in fluorescence buffer. Competing ligands were added in stepwise fashion. After a short equilibration time, the anisotropy of



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the RH6G emission was measured. Each anisotropy reading had an integration time of 15 seconds an averaged value.

Direct competitive binding at a single site between RH6G (1) and other ligands is described by the equilibria (equations 2 and 3): KD1   → PLF P + LF ← 

(2)

KD2   → PL P + L← 

(3),

where P is BmrR, LF is RH6G, L is the competing ligand and KD1 and KD2 are the dissociation constants for RH6G and L, respectively. Using mass balance and the equilibria above (equations 2 and 3), one can derive an equation (cubic in [PLF]) and determine its roots, that relate [PLF] (or fraction LF bound, FPLF) to both [L]T and KD2 (see equation 16)63, 64 Furthermore, the measured anisotropy (r) of the RH6G (1) emission relates to FPLF as shown by equation 4:

r = FPLF × rPLF + (1− FPLF ) × rLF

(4),

where rLF is the anisotropy of the free RH6G and rPLF is the anisotropy of the BmrR bound RH6G. The KD2 values for the different ligands were determined from global, nonlinear least square fits to the system of the equations, including a root of cubic


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binding equation, [L]T and KD2 and equation 4. Mathematica (Wolfram Research) was used to determine KD2 for all ligands. KD1 (520 nM) was determined previously by multiple, independent methods. The reported KD2 values are the result of at least two to three experiments. The errors in the curve fit were directly determined (Mathematica, Kaleidagraph). The final reported errors were determined by the propagation of errors.

Molecular docking analyses of MD recognition. Docking simulations were carried out using Autodock 4.265, 66. All simulations included flexible ligands and limited protein flexibility at N149, which is shown to adopt multiple, distinct rotamers in the BmrR structures solved to date. Each of the remaining pocket residues, in general, were fixed in a single conformation. The BmrR model (pdb ID: 3q3d) was prepared for docking using the Structure Preparation tool (MOE, Chemical Computing Group) 67 67 67 67 67 67 67 67 6767

and YASARA68. The latter was used to energy minimize the BmrR

model via simulated annealing with explicit solvent and an optimized force field based on AMBER (YAMBER3). Ligand coordinates were obtained from LigandExpo or generated using the Builder tool in MOE. Ligand ionization and atom partial charges



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(Gasteiger (PEOE)) were also assigned using MOE. Ligands and BmrR model were further processed using Autodock 4.2. All ligands were docked to the BmrR structure using a 80 x 80 x 80 point grid with a spacing of 0.375, centered at the ligandpocket. The Lemarckian algorithm was used to search for low energy ligand binding modes. The protocol included 100 GA runs and a maximum of 2.6 x 106 evaluations. Each docking simulation began with a randomly positioned ligand. The resulting poses were clustered (2.0 Å threshold) and ranked based on the lowest binding energy pose. In nearly all cases, the docking simulations returned multiple clusters with predicted binding energies within ca. 1.0 kcal/mol of the lowest energy cluster. In our analyses of the BmrR interaction with each ligand, we focused on the lowest energy pose of the docking clusters.

Analyses of multiple ligand-binding modes

focused on representative poses of the low energy clusters (i.e. lowest energy docking pose).

Quantification of ligand interactions: Ligand-pocket and contacts. Contacts surface areas and electrostatic interaction distances were quantitatively analyzed using LPC server and software (Weizmann Institute of Science). Ligand surface area


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burial and ligand-receptor shape complementarity were calculated using PDBePISA server and the program, SC (CCP4), respectively69, 70. Interaction potential map (MOE) is based on a three-term interaction energy for each point on a rectilinear grid (equation 5):

E xyz = ∑ Elj + Eele + Ehb

(5),

where Elj is the Leonard-Jones potential, Eele is given by a reaction field formulation and Ehb is a hydrogen bonding term that is a product of a radial and angular term. Hydrophobic energies are calculated at each grid point using a modified energy (equation 6):

Exyz = ∑ Elj + S − Ehb

(6),

where S (the entropy term) reflects favorable interactions between hydrophobic particles and water. The Ehb term reflects the costs of disrupting HB networks in the solvent and protein hydration shells. BmrR was ionized and charged using Protonate 3D (MOE) and CHARMM27 atomic partial charges (MOE). The non-bonded contact maps (MOE) reveal the preferred locations for hydrophobic and hydrophilic ligand atoms using fitted distributions from crystallographic data.



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Molecular Graphics. Pymol was used for the analyses of structures and generation of structural models for figures71.

Acknowledgements S.B. is a NSF graduate research fellowship awardee (DGE-0707427). H.W. is a recipient of the Arnold and Mabel Beckman Young Investigator Award. This work is also supported by a NSF CAREER Award to H.W. (MCB-0953430).

ASSOCIATED CONTENT Supporting Information The supporting information is available free of charge on the ACS Publications website. It includes one figure (PDF).

AUTHOR INFORMATION Corresponding author* [email protected] Telephone: 410-502-5629


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Funding Sources No competing financial interests have been declared. This work has been funded by the following sources. S.B. is a NSF graduate research fellowship awardee (DGE-0707427). H.W. is a recipient of the Arnold and Mabel Beckman Young Investigator Award. This work is also supported by a NSF CAREER Award to H.W. (MCB-0953430).



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FOOTNOTES The abbreviations used are: MDR, multidrug resistance; MD, multidrug; vdW, van der Waals, LEDP, lowest energy docking pose; RSCC, real space correlation coefficient; LBM, ligandbinding modes; DME, drug-metabolizing enzymes; HB, hydrogen-bonding; PGP, pglycoprotein.

FIGURE LEGENDS Figure 1. Homodimeric BmrR and the MDR and nonMDR ligand sets. (A) BmrR is bound to 8 and DNA. The DNA-binding (violet), dimerization (blue) and effector-recognition (grey) domains are depicted as ribbons and cylinders. Tyr residues are shown as green spheres. (B) Chemical probes (MDR and nonMDR series) of MD recognition. Ligand numbers follow the scheme: cations (blue), charge-neutral compounds (yellow), polycations (green) and anions (red).

Figure 2. Two fluorescence-based approaches enable broad detection of chemical recognition by BmrR. (A and B) Binding by 2 and 11 quenches the intrinsic fluorescence in


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BmrR (see insets). (C and D) Binding by 14 and 19 reduces the fluorescence anisotropy (λEX = 495 nm, λEM = 525 nm) of the 1 emission and displaces it from the BmrR ligand-pockets. Numerical fits to the binding isotherm data for all ligands are consistent with the identical, independent sites model.

See Materials and Methods section.

Figure 3. Molecular docking reproduces structural aspects of MD recognition. (A) Relative ligand positions for the observed (grey sticks) and docked (colored) structures. RMSD values are reported in the text. The observed and predicted binding modes for (B) 5 and (C) 11 reveal different, yet chemically equivalent contacts. HB residues are labeled with the contacts shown as orange (observed) and yellow (predicted) dashed lines.

Figure 4. (top) Molecular docking reproduces solution-binding aspects of MD recognition. Plot comparisons of observed—predicted (A) ligand rankings and (B) binding energies show linear correlations. Two outliers are shown in A as blue squares. (C and D) Docking further elucidates electrostatic and HB binding determinants. (C) E145 expands the carboxylate triad to a tetrad. The cationic center of 4 (purple) approximates the pocket center with all others (blue spheres) showing close-to-moderate approaches to D47’, E145, E253 and E266. (D)



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Docking identifies previously unreported ligand accessible HB sites. They include: carboxylate (red), tyrosyl (violet), main-chain (green) and amide (grey) oxygen atoms; amide (grey) and lysyl nitrogen (blue) atoms are also observed.

Figure 5. Tabular representation of predicted BmrR—ligand interactions. Residues lining the ligand-pocket define key determinants of MD recognition, including the aromatic docking platform (purple), carboxylate tetrad (red), hydrophobic pincer (orange) and HB sites of the ligand solvation shell are not explicitly shown. Also presented are second-shell residues (grey), including important HB contributors (blue), N149 and P226.

Figure 6. Key features of MD recognition are revealed through analyses of (A) X-ray structures, (B) docking predictions and (C) multiple alternative ligand-binding modes. The first two panels present composite views of the bound ligand modes detected by X-ray diffraction and docking (LEDPs) approaches. The last panel presents the low energy binding modes predicted for 12. The circled numbers are shown to emphasize HB differences between the three views.


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Figure 7. Crystallographic data suggest multiple ligand-binding modes for (A) 5, (B) 18, (C) 8 and (D) 11. All panels show positive, unbiased Fo-Fc difference maps (green mesh, 1.50), superimposed with three refined, LBMs for 5, 18, 8 and 11. Previously reported LBMs (grey sticks) are shown with two others (orange and pink) identified by docking and model building. The real space correlation coefficients (RSCC) and B-factors (parentheses) are presented for the previously reported (bold) and model-based LBMs. The RSCC and Bfactor values are color-coded according to the stick representations in the figure.

Figure 8. Interplay between ligand structure and binding. All analyses (except D) exclude notable outliers (see logKD-clogP analyses). D includes three outlier ligands, 19 – 21, due to their direct relevance to the analysis. (top) Analyses of ΔGBIND versus: number of ligand (A) HB acceptors, (B) nitrogen, (C) oxygen and (D) aromatic atoms and (E) ligand hydrophobic vdW surface area (VSA). (bottom) (F) A linear logKD—clogP relationship is observed for an extended set of BmrR probes; 22-27 (green), 4 (purple) and 19, 20, 10 and 14 (orange) are outliers (see text for explanation). (G) A linear logKD—clogP correlations is also observed for QacR probes. (H) Box plot analyses of ligand charge preference for BmrR. The median ΔGBIND value for the anionic (red), neutral (yellow), cationic (blue) and polycationic (green)



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ligand subgroups is indicated by the line that separates the second and third quartiles (two boxes): 11 is the polycation outlier. (I) Salt-dependencies of 1-RH6G+1 (blue), 22-SCM+2 (green) and 26-PAR+5 (red) binding show linear ΔGBIND—ln[KCl] correlations.

Figure 9. Interaction potentials and contact preferences implicate a major role for polar interactions in MD recognition. Residues shown are found within a 6 Å radius of 8 (orange sticks). (A) Interaction potentials were calculated for HOH (magenta) and hydrophobic (green) probes (see Experimental Procedure section). The HOH and hydrophobic maps are contoured at -5 and -2.5 kcal/mol, respectively. (B) Contact preference maps reflect the probabilities for observing interactions with N and O (magenta) and C and S (green) atom types. The 90% iso-contour level is shown in each case.


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ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FIGURE 1

A

C

B


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FIGURE 2

A

C

B

D



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FIGURE 3


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FIGURE 4



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FIGURE 5


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FIGURE 6

A

B

Val147 k

l

m

C

Tyr152 k

l

Tyr170 m n Tyr187

Tyr229

j Asn149

Tyr268 Ile255

Pro144

Phe224


m

l 0.0 0.2 0.4 0.6 0.8 1.0

n

FIGURE 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48



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FIGURE 8



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FIGURE 9


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Solution-Binding and Molecular Docking Approaches Combine to

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